A. A. N. Perwira Redi, Gerlyn Calica Altes, Justine Kyle Coronel Chan, Arni C. Acla, Parida Jewpanya, A. A. N. Agung Redioka, Yogi Tri Prasetyo, M. N. Young
{"title":"Facility Location Problem to Identify The Optimal Allocation of Near-Expired COVID-19 Vaccines","authors":"A. A. N. Perwira Redi, Gerlyn Calica Altes, Justine Kyle Coronel Chan, Arni C. Acla, Parida Jewpanya, A. A. N. Agung Redioka, Yogi Tri Prasetyo, M. N. Young","doi":"10.1109/DASA54658.2022.9765157","DOIUrl":null,"url":null,"abstract":"Coronavirus 2019, popularly known as COVID-19 and declared a pandemic by the World Health Organization (WHO) in 2020, has affected billions of people and claimed millions of lives. Leaders and corporations worldwide have worked feverishly to develop a vaccine to combat the virus. After numerous tests and trials, COVID-19 vaccines were developed. Given the magnitude of the need for vaccination, these vaccines should not go to waste due to expiration from slow-paced rollouts or oversupply. This study aims to maximize near-expired COVID-19 vaccines in cases of oversupply by distributing them in neighbouring facilities at a low delivery cost and by utilizing P-median modelling. All gathered data were loaded into and run through the AMPL simulation model, with varying P-values or the number of facilities to be located to act as suppliers to the remaining demand nodes. Following the model simulation, it was observed that the P-value is inversely proportional to the cost; therefore, the cost of delivering near-expired COVID-19 vaccines to the demand clusters decreases as the P-value increases. Through the simulation model, the researchers determined which node facilities, if opened, would incur the lowest delivery cost.","PeriodicalId":231066,"journal":{"name":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DASA54658.2022.9765157","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Coronavirus 2019, popularly known as COVID-19 and declared a pandemic by the World Health Organization (WHO) in 2020, has affected billions of people and claimed millions of lives. Leaders and corporations worldwide have worked feverishly to develop a vaccine to combat the virus. After numerous tests and trials, COVID-19 vaccines were developed. Given the magnitude of the need for vaccination, these vaccines should not go to waste due to expiration from slow-paced rollouts or oversupply. This study aims to maximize near-expired COVID-19 vaccines in cases of oversupply by distributing them in neighbouring facilities at a low delivery cost and by utilizing P-median modelling. All gathered data were loaded into and run through the AMPL simulation model, with varying P-values or the number of facilities to be located to act as suppliers to the remaining demand nodes. Following the model simulation, it was observed that the P-value is inversely proportional to the cost; therefore, the cost of delivering near-expired COVID-19 vaccines to the demand clusters decreases as the P-value increases. Through the simulation model, the researchers determined which node facilities, if opened, would incur the lowest delivery cost.